Unraveling the impact of financial stress and trade policy uncertainty on advancing renewable energy transition in the USA
Bibliographic record
Abstract
Renewable energy consumption (REC) has become the most suitable option to tackle the issues of energy security and climate change because it is a sustainable, clean, and affordable energy source. Literature on the determinants of REC is growing rapidly, but most rely on linear analysis. This analysis is a nonlinear perspective on the impact of financial stress and trade policy uncertainty on REC in the USA over 1995Q1-2021Q4. The study uses autoregressive distributed lag and nonlinear autoregressive distributed lag for empirical analysis. The linear estimates reveal that financial stress and trade policy uncertainty reduce long-run (LR) REC. On the other hand, the nonlinear estimates suggest that positive changes in financial stress and trade policy uncertainty reduce REC, whereas the negative changes in both these factors boost REC in the LR. While the GDP causes an improvement in REC, environmental technologies do not significantly impact the REC in the LR. In the short-run, only the linear and nonlinear estimates of financial stress and environmental technologies significantly impact REC. Due to the asymmetric nature of the findings, policymakers must take into account the positive and negative changes in the financial stress and trade policy uncertainty while devising policies to promote renewable energy transition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".